System for wagering on event outcomes based on two timings during an event

ABSTRACT

A system for wagering on event outcomes based on two timings during an event can include determining a first timing of an event and a plurality of possible future states of the event based upon a second timing of the event. Then the system may determine a probability of occurrence for each of the plurality of possible future states based on probability information. Then the system may determine odds for betting that a first one of the pluralities of possible future states will occur. Then the system may present a plurality of users at a plurality of corresponding computer interfaces information indicating an opportunity to place a bet at the determined odds that the first possible future state will occur, the plurality of computer interfaces being in networked communication with the at least one processor.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present patent application claims benefit and priority to U.S.Provisional Patent Application No. 63/253,223 filed on Oct. 7, 2021,which is hereby incorporated by reference into the present disclosure.

FIELD

The present disclosure generally relates to in-play wagering on livesporting events.

BACKGROUND

Currently, on wagering applications and wagering platforms, users arelimited in their wagering options to only wager on games' outcomes orgame-defined time periods.

Also, users are limited in the types of wagering options available oncean event has started.

Lastly, users are routinely offered variations of the same types ofwagering options from various wagering applications and wageringplatforms enticing users to place a wager.

Thus, there is a need in the prior art to provide users with wageringoptions during an event.

BRIEF DESCRIPTIONS OF THE DRAWINGS

The accompanying drawings illustrate various embodiments of systems,methods, and various other aspects of the embodiments. Any person withordinary art skills will appreciate that the illustrated elementboundaries (e.g., boxes, groups of boxes, or other shapes) in thefigures represent an example of the boundaries. It may be understoodthat, in some examples, one element may be designed as multiple elementsor that multiple elements may be designed as one element. In someexamples, an element shown as an internal component of one element maybe implemented as an external component in another and vice versa.Furthermore, elements may not be drawn to scale. Non-limiting andnon-exhaustive descriptions are described with reference to thefollowing drawings. The components in the figures are not necessarily toscale, emphasis instead being placed upon illustrating principles.

FIG. 1 illustrates a system for wagering on event outcomes based on twotimings during an event, according to an embodiment.

FIG. 2 illustrates a base module, according to an embodiment.

FIG. 3 illustrates an example of OCM Object Content, according to anembodiment.

FIG. 4 illustrates an example 2 of OCM Object Content, according to anembodiment.

DETAILED DESCRIPTION

Aspects of the present invention are disclosed in the followingdescription and related figures directed to specific embodiments of theinvention. Those of ordinary skill in the art will recognize thatalternate embodiments may be devised without departing from the spiritor the scope of the claims. Additionally, well-known elements ofexemplary embodiments of the invention will not be described in detailor will be omitted so as not to obscure the relevant details of theinvention.

As used herein, the word exemplary means serving as an example, instanceor illustration. The embodiments described herein are not limiting, butrather are exemplary only. The described embodiments are not necessarilyto be construed as preferred or advantageous over other embodiments.Moreover, the terms embodiments of the invention, embodiments, orinvention do not require that all embodiments of the invention includethe discussed feature, advantage, or mode of operation.

Further, many of the embodiments described herein are described in termsof sequences of actions to be performed by, for example, elements of acomputing device. It should be recognized by those skilled in the artthat specific circuits can perform the various sequence of actionsdescribed herein (e.g., application specific integrated circuits(ASICs)) and/or by program instructions executed by at least oneprocessor. Additionally, the sequence of actions described herein can beembodied entirely within any form of computer-readable storage mediumsuch that execution of the sequence of actions enables the processor toperform the functionality described herein. Thus, the various aspects ofthe present invention may be embodied in several different forms, all ofwhich have been contemplated to be within the scope of the claimedsubject matter. In addition, for each of the embodiments describedherein, the corresponding form of any such embodiments may be describedherein as, for example, a computer configured to perform the describedaction.

With respect to the embodiments, a summary of terminology used herein isprovided.

An action refers to a specific play or specific movement in a sportingevent. For example, an action may determine which players were involvedduring a sporting event. In some embodiments, an action may be a throw,shot, pass, swing, kick, and/or hit performed by a participant in asporting event. In some embodiments, an action may be a strategicdecision made by a participant in the sporting event, such as a player,coach, management, etc. In some embodiments, an action may be a penalty,foul, or other type of infraction occurring in a sporting event. In someembodiments, an action may include the participants of the sportingevent. In some embodiments, an action may include beginning events ofsporting event, for example opening tips, coin flips, opening pitch,national anthem singers, etc. In some embodiments, a sporting event maybe football, hockey, basketball, baseball, golf, tennis, soccer,cricket, rugby, MMA, boxing, swimming, skiing, snowboarding, horseracing, car racing, boat racing, cycling, wrestling, Olympic sport,eSports, etc. Actions can be integrated into the embodiments in avariety of manners.

A “bet” or “wager” is to risk something, usually a sum of money, againstsomeone else's or an entity based on the outcome of a future event, suchas the results of a game or event. It may be understood thatnon-monetary items may be the subject of a “bet” or “wager” as well,such as points or anything else that can be quantified for a “bet” or“wager.” A bettor refers to a person who bets or wagers. A bettor mayalso be referred to as a user, client, or participant throughout thepresent invention. A “bet” or “wager” could be made for obtaining orrisking a coupon or some enhancements to the sporting event, such asbetter seats, VIP treatment, etc. A “bet” or “wager” can be made forcertain amount or for a future time. A “bet” or “wager” can be made forbeing able to answer a question correctly. A “bet” or “wager” can bemade within a certain period. A “bet” or “wager” can be integrated intothe embodiments in a variety of manners.

A “book” or “sportsbook” refers to a physical establishment that acceptsbets on the outcome of sporting events. A “book” or “sportsbook” systemenables a human working with a computer to interact, according to set ofboth implicit and explicit rules, in an electronically powered domain toplace bets on the outcome of sporting event. An added game refers to anevent not part of the typical menu of wagering offerings, often postedas an accommodation to patrons. A “book” or “sportsbook” can beintegrated into the embodiments in a variety of manners.

To “buy points” means a player pays an additional price (more money) toreceive a half-point or more in the player's favor on a point spreadgame. Buying points means you can move a point spread, for example, upto two points in your favor. “Buy points” can be integrated into theembodiments in a variety of manners.

The “price” refers to the odds or point spread of an event. To “take theprice” means betting the underdog and receiving its advantage in thepoint spread. “Price” can be integrated into the embodiments in avariety of manners.

“No action” means a wager in which no money is lost or won, and theoriginal bet amount is refunded. “No action” can be integrated into theembodiments in a variety of manners.

The “sides” are the two teams or individuals participating in an event:the underdog and the favorite. The term “favorite” refers to the teamconsidered most likely to win an event or game. The “chalk” refers to afavorite, usually a heavy favorite. Bettors who like to bet bigfavorites are referred to “chalk eaters” (often a derogatory term). Anevent or game in which the sportsbook has reduced its betting limits,usually because of weather or the uncertain status of injured players,is referred to as a “circled game.” “Laying the points or price” meansbetting the favorite by giving up points. The term “dog” or “underdog”refers to the team perceived to be most likely to lose an event or game.A “longshot” also refers to a team perceived to be unlikely to win anevent or game. “Sides,” “favorite,” “chalk,” “circled game,” “laying thepoints price,” “dog,” and “underdog” can be integrated into theembodiments in a variety of manners.

The “money line” refers to the odds expressed in terms of money. Withmoney odds, whenever there is a minus (−), the player “lays” or is“laying” that amount to win (for example, $100); where there is a plus(+), the player wins that amount for every $100 wagered. A “straightbet” refers to an individual wager on a game or event that will bedetermined by a point spread or money line. The term “straight-up” meanswinning the game without any regard to the “point spread,” a“money-line” bet. “Money line,” “straight bet,” and “straight-up” can beintegrated into the embodiments in a variety of manners.

The “line” refers to the current odds or point spread on a particularevent or game. The “point spread” refers to the margin of points inwhich the favored team must win an event by to “cover the spread.” To“cover” means winning by more than the “point spread.” A handicap of the“point spread” value is given to the favorite team so bettors can choosesides at equal odds. “Cover the spread” means that a favorite wins anevent with the handicap considered or the underdog wins with additionalpoints. To “push” refers to when the event or game ends with no winneror loser for wagering purposes, a tie for wagering purposes. A “tie” isa wager in which no money is lost or won because the teams' scores wereequal to the number of points in the given “point spread.” The “openingline” means the earliest line posted for a particular sporting event orgame. The term “pick” or “pick 'em” refers to a game when neither teamis favored in an event or game. “Line,” “cover the spread,” “cover,”“tie,” “pick,” and “pick-em” can be integrated into the embodiments in avariety of manners.

To “middle” means to win both sides of a game; wagering on the“underdog” at one point spread and the favorite at a different pointspread and winning both sides. For example, if the player bets theunderdog +4½ and the favorite −3½ and the favorite wins by 4, the playerhas middled the book and won both bets. “Middle” can be integrated intothe embodiments in a variety of manners.

Digital gaming refers to any type of electronic environment that can becontrolled or manipulated by a human user for entertainment purposes. Asystem that enables a human and a computer to interact according to setof both implicit and explicit rules in an electronically powered domainfor the purpose of recreation or instruction. “eSports” refers to a formof sports competition using video games, or a multiplayer video gameplayed competitively for spectators, typically by professional gamers.Digital gaming and “eSports” can be integrated into the embodiments in avariety of manners.

The term event refers to a form of play, sport, contest, or game,especially one played according to rules and decided by skill, strength,or luck. In some embodiments, an event may be football, hockey,basketball, baseball, golf, tennis, soccer, cricket, rugby, MMA, boxing,swimming, skiing, snowboarding, horse racing, car racing, boat racing,cycling, wrestling, Olympic sport, etc. The event can be integrated intothe embodiments in a variety of manners.

The “total” is the combined number of runs, points or goals scored byboth teams during the game, including overtime. The “over” refers to asports bet in which the player wagers that the combined point total oftwo teams will be more than a specified total. The “under” refers tobets that the total points scored by two teams will be less than acertain figure. “Total,” “over,” and “under” can be integrated into theembodiments in a variety of manners.

A “parlay” is a single bet that links together two or more wagers; towin the bet, the player must win all the wagers in the “parlay.” If theplayer loses one wager, the player loses the entire bet. However, ifthey win all the wagers in the “parlay,” the player receives a higherpayoff than if the player had placed the bets separately. A “roundrobin” is a series of parlays. A “teaser” is a type of parlay in whichthe point spread, or total of each individual play is adjusted. Theprice of moving the point spread (teasing) is lower payoff odds onwinning wagers. “Parlay,” “round robin,” “teaser” can be integrated intothe embodiments in a variety of manners.

A “prop bet” or “proposition bet” means a bet that focuses on theoutcome of events within a given game. Props are often offered onmarquee games of great interest. These include Sunday and Monday nightpro football games, various high-profile college football games, majorcollege bowl games, and playoff and championship games. An example of aprop bet is “Which team will score the first touchdown?” “Prop bet” or“proposition bet” can be integrated into the embodiments in a variety ofmanners.

A “first-half bet” refers to a bet placed on the score in the first halfof the event only and only considers the first half of the game orevent. The process in which you go about placing this bet is the sameprocess that you would use to place a full game bet, but as previouslymentioned, only the first half is important to a first half bet type ofwager. A “half-time bet” refers to a bet placed on scoring in the secondhalf of a game or event only. “First-half-bet” and “half-time-bet” canbe integrated into the embodiments in a variety of manners.

A “futures bet” or “future” refers to the odds that are posted well inadvance on the winner of major events. Typical future bets are the ProFootball Championship, Collegiate Football Championship, the ProBasketball Championship, the Collegiate Basketball Championship, and thePro Baseball Championship. “Futures bet” or “future” can be integratedinto the embodiments in a variety of manners.

The “listed pitchers” is specific to a baseball bet placed only if bothpitchers scheduled to start a game start. If they do not, the bet isdeemed “no action” and refunded. The “run line” in baseball refers to aspread used instead of the money line. “Listed pitchers,” “no action,”and “run line” can be integrated into the embodiments in a variety ofmanners.

The term “handle” refers to the total amount of bets taken. The term“hold” refers to the percentage the house wins. The term “juice” refersto the bookmaker's commission, most commonly the 11 to 10 bettors lay onstraight point spread wagers: also known as “vigorish” or “vig”. The“limit” refers to the maximum amount accepted by the house before theodds and/or point spread are changed. “Off the board” refers to a gamein which no bets are being accepted. “Handle,” “juice,” vigorish,”“vig,” and “off the board” can be integrated into the embodiments in avariety of manners.

“Casinos” are a public room or building where gambling games are played.“Racino” is a building complex or grounds having a racetrack andgambling facilities for playing slot machines, blackjack, roulette, etc.“Casino” and “Racino” can be integrated into the embodiments in avariety of manners.

Customers are companies, organizations or individuals that would deploy,for fees, and may be part of, or perform, various system elements ormethod steps in the embodiments.

Managed service user interface service is a service that can helpcustomers (1) manage third parties, (2) develop the web, (3) performdata analytics, (4) connect thru application program interfaces and (4)track and report on player behaviors. A managed service user interfacecan be integrated into the embodiments in a variety of manners.

Managed service risk management service are services that assistcustomers with (1) very important person management, (2) businessintelligence, and (3) reporting. These managed service risk managementservices can be integrated into the embodiments in a variety of manners.

Managed service compliance service is a service that helps customersmanage (1) integrity monitoring, (2) play safety, (3) responsiblegambling, and (4) customer service assistance. These managed servicecompliance services can be integrated into the embodiments in a varietyof manners.

Managed service pricing and trading service is a service that helpscustomers with (1) official data feeds, (2) data visualization, and (3)land based on property digital signage. These managed service pricingand trading services can be integrated into the embodiments in a varietyof manners.

Managed service and technology platforms are services that helpcustomers with (1) web hosting, (2) IT support, and (3) player accountplatform support. These managed service and technology platform servicescan be integrated into the embodiments in a variety of manners.

Managed service and marketing support services are services that helpcustomers (1) acquire and retain clients and users, (2) provide forbonusing options, and (3) develop press release content generation.These managed service and marketing support services can be integratedinto the embodiments in a variety of manners.

Payment processing services are services that help customers with (1)account auditing and (2) withdrawal processing to meet standards forspeed and accuracy. Further, these services can provide for integrationof global and local payment methods. These payment processing servicescan be integrated into the embodiments in a variety of manners.

Engaging promotions may allow customers to treat players to free bets,odds boosts, enhanced access, and flexible cashback to boost lifetimevalue. Engaging promotions can be integrated into the embodiments in avariety of manners.

“Cash out” or “pay out” or “payout” may allow customers to makeavailable, on singles bets or accumulated bets with a partial cash outwhere each operator can control payouts by always managing commissionand availability. The “cash out” or “pay out” or “payout” can beintegrated into the embodiments in a variety of manners, including bothmonetary and non-monetary payouts, such as points, prizes, promotionalor discount codes, and the like.

“Customized betting” may allow customers to have tailored personalizedbetting experiences with sophisticated tracking and analysis of players’behavior. “Customized betting” can be integrated into the embodiments ina variety of manners.

Kiosks are devices that offer interactions with customers, clients, andusers with a wide range of modular solutions for both retail and onlinesports gaming. Kiosks can be integrated into the embodiments in avariety of manners.

Business Applications are an integrated suite of tools for customers tomanage the everyday activities that drive sales, profit, and growth bycreating and delivering actionable insights on performance to helpcustomers to manage the sports gaming. Business Applications can beintegrated into the embodiments in a variety of manners.

State-based integration may allow for a given sports gambling game to bemodified by states in the United States or other countries, based uponthe state the player is in, mobile phone, or other geolocationidentification means. State-based integration can be integrated into theembodiments in a variety of manners.

Game Configurator may allow for configuration of customer operators tohave the opportunity to apply various chosen or newly created businessrules on the game as well as to parametrize risk management. The GameConfigurator can be integrated into the embodiments in a variety ofmanners.

“Fantasy sports connectors” are software connectors between method stepsor system elements in the embodiments that can integrate fantasy sports.Fantasy sports allow a competition in which participants selectimaginary teams from among the players in a league and score pointsaccording to the actual performance of their players. For example, if aplayer in fantasy sports is playing at a given real-time sport, oddscould be changed in the real-time sports for that player.

Software as a service (or SaaS) is a software delivery and licensingmethod in which software is accessed online via a subscription ratherthan bought and installed on individual computers. Software as a servicecan be integrated into the embodiments in a variety of manners.

Synchronization of screens means synchronizing bets and results betweendevices, such as TV and mobile, PC, and wearables. Synchronization ofscreens can be integrated into the embodiments in a variety of manners.

Automatic content recognition (ACR) is an identification technology thatrecognizes content played on a media device or present in a media file.Devices containing ACR support enable users to quickly obtain additionalinformation about the content they see without any user-based input orsearch efforts. A short media clip (audio, video, or both) may beselected to start the recognition. This clip could be selected fromwithin a media file or recorded by a device. Through algorithms such asfingerprinting, information from the actual perceptual content is takenand compared to a database of reference fingerprints, wherein eachreference fingerprint corresponds with a known recorded work. A databasemay contain metadata about the work and associated information,including complementary media. If the media clip's fingerprint ismatched, the identification software returns the corresponding metadatato the client application. For example, during an in-play sports game, a“fumble” could be recognized and at the time stamp of the event,metadata such as “fumble” could be displayed. Automatic contentrecognition (ACR) can be integrated into the embodiments in a variety ofmanners.

Joining social media means connecting an in-play sports game bet orresult to a social media connection, such as a FACEBOOK® chatinteraction. Joining social media can be integrated into the embodimentsin a variety of manners.

Augmented reality means a technology that superimposes acomputer-generated image on a user's view of the real world, thusproviding a composite view. In an example of this invention, a real timeview of the game can be seen and a “bet”—which is a computer-generateddata point—is placed above the player that is bet on. Augmented realitycan be integrated into the embodiments in a variety of manners.

A betting exchange system may be a platform that matches up users whowish to take opposite sides in a bet. Users may “back” or “lay” wagerson the outcome of an event or a portion of the event. Each wager on abetting exchange may involve two bets, one backing, and one laying. Backbetting, or “backing” a selection, is to wager that the outcome willoccur. Lay betting, or “laying” a selection, is to wager that theoutcome will not occur. Users may then trade those positions up untilthe point that the wagering market closes, and the wagers are paid out.The value of a wager may increase or decrease based as a sporting eventprogresses. Exchanges may allow users to cash out of their positionbefore the market for a wager closes by selling that wager at thecurrent price to another user on the exchange.

Betting exchange systems may allow users to wager on what is not goingto happen with “lay” wagers. Often, users are more likely to win moneyby betting on what is not going to happen. Take the correct scoremarkets in soccer, for example. Consistently picking the exact score ina game is impossible. One may get it right every now and then, but itjust comes down to luck due to the number of options from which tochoose. There are nine potential score outcomes even if we could ruleout either team scoring more than two goals.

Betting exchange systems may allow wagers involving more than two usersas exchange betting may allow for one lay bet to be backed by multipleusers who each back a portion of the lay bet. Those wagers may be atdifferent odds. For example, a first user may want to back Team A to winfor $20. There may be a second user, or users, who want to lay $10 onTeam A not to win at 2 to 1 odds. There may also be a third user, orusers, who want to lay $10 on Team A not to win at 3 to 1 odds. Thefirst user may back team A to win for $10 at the best available odds, inthis case, 2 to 1. If the first user wants to back team A to win for$20, they will need to back ten dollars at 2 to 1 against the seconduser and back the other ten dollars against the third user at 3 to 1.This combination of wagers is the equivalent backing Team A to win for$20 at 2.5 to 1 odds.

Betting exchange systems may not take on the risk of any given wager asa traditional sportsbook would because the exchange users set the odds.Removing the risk to the wagering platform may allow users to get morevalue out of a wager by paying less to the exchange that does not haveto take on the risk that a sportsbook must price into each wager. Theremay not be an inherent limit to the stakes or odds that a user of abetting exchange can propose. Betting exchange systems may deriverevenue from wagers differently than traditional sportsbooks. Revenuemay be based on the volume of wagers and trades on their platform,removing the results of the wager immaterial to the betting exchangesystem operation. Betting exchange systems may not lay bets themselvesbut instead might rely on users to offer up wagers. The betting exchangesystem's role may then be to facilitate the exchange of wager terms,trades of wagers, and settlement of wagers.

Betting exchange systems may not tend to limit or ban successful usersthe way traditional sportsbooks do. Betting exchange systems may notlimit or ban successful users because there is no impact to the bettingexchange system from a user's success. A successful user needs only tofind someone to take the other side of their wager. A betting exchangesystem benefits from the increased liquidity brought to markets bysuccessful gamblers.

Betting exchange systems may not limit the wagers they can offer. Atraditional sportsbook may only offer wagers on which they havecalculated odds. Users of a betting exchange system may create their ownmarkets for any outcome and odds that have at least one user to back andat least one user to lay for a given outcome. Users may also be able towager at a different price than the market price. For example, if a useris confident the price on a team they want to back is going to drift toa bigger price due to team news, they can place a request and set ahigher price than is currently available. This may prompt another userto think they are wrong about their estimation and be prepared to matchtheir bet at the bigger price.

Betting exchange systems may present information related to the exchangeand potential wagers to back or lay in several different ways. Somebetting exchange systems may use a standard or grid interface that putsthe back and lay options laid out left to right, with the prices gettinghigher as you move away from the center. The amount of money or actionat a given back or lay price may often be displayed. Some bettingexchange systems may offer an option to back all or lay all. This optionmay allow a user to back or lay an outcome at multiple different prices.A user may not need to back all or lay all to wager at multiple priceson a given outcome.

A “ladder” interface may be a view in which the full market depth of amarket on a betting exchange system is shown, along with the valuesassociated with that price (volume already traded, amounts available,etc.). This type of interface may enable a user to see where the markethas been and help them evaluate where it might be heading in the shortterm. Users may define a default “stake” or wager amount that, oncedefined, may allow the user to place orders immediately with a singleclick on the back or lay option at the price at which the user wants toenter the market. Users may remove their stake in the same fashion ifanother user has not yet accepted the stake. Ladder interfaces may allowusers to place many trades in a short time. This trading volume mayallow users to win, not only if their selection is successful, but byhedging their position across all possible outcomes. Each priceincrement (tick) on the ladder may display to the user their financialposition if they closed at that point. Some betting exchange systems mayshow a graphical representation of where the selection has been matched.Others may show the user where they are in the queue of contracts to bemet. Third-party software providers may receive data from the bettingexchange system through an API to allow users to customize theirinterface and functionality. These third-party software programs mayalso allow users to incorporate additional data feeds, such as a newsfeed related to the live sporting event, into the user's wageringinterface.

A betting exchange system may offer users multiple ways to win. Usersmay be able to use automated bots to manage their betting activity.Users who lack the expertise to create bots may set up betting triggersthat may automate certain betting behaviors when specific market pricesare met. Users may engage in “position trading” wherein bets may beplaced with the intent to sell them off, seeking to find opportunitiesin market swings. Betting exchanges may allow users “hedging” optionsthat may incorporate one or more of these strategies to mitigate risk.Liquidity in betting exchange systems may be limited by regulations thatrestrict participants in an exchange bet. Therefore, a betting exchangesystem may take steps to maximize the amount of liquidity on theirplatform to ensure the most markets are available.

A betting exchange system may rely on liquidity to ensure marketavailability. Markets may only be available if there is someone to bothback and lay that market. There may be fewer markets available on abetting exchange if fewer people offer odds, and fewer people offer oddsif fewer people accept them. If the people are not offering odds andthere is no traditional bookmaker to do it, their markets may not becreated, and wagers may not be placed.

A machine learning betting system may be a system that incorporatesmachine learning into at least one step in the odds makings, marketcreation, user interface, or personalization of a sports wageringplatform. Machine learning may leverage artificial intelligence to allowa computer algorithm to improve itself automatically over time withoutbeing explicitly programmed. Machine learning and AI may often discussedtogether, and the terms may sometimes used interchangeably, but they arenot the same. An important distinction is that although all machinelearning is AI, not all AI is machine learning. Machine learningalgorithms may develop their framework for analyzing a data set throughexperience in using that data. Machine learning may help create modelsthat can process and analyze large amounts of complex data to deliveraccurate results. Machine learning may use models or mathematicalrepresentations of real-world processes. It may achieve this throughexamining features, measurable properties, and parameters of a data set.It may utilize a feature vector, or a set of multiple numeric features,as a training input for prediction purposes. An algorithm may take a setof data known as “training data” as input. The learning algorithm mayfind patterns in the input data and train the model for expected results(target). The output of the training process may be the machine learningmodel. A model may then make a prediction when fed input data. The valuethat the machine learning model must predict is called the target orlabel. When excessively large amounts of data are fed to a machinelearning algorithm, it may experience overfitting, a situation in whichthe algorithm may learn from noise and inaccurate data entries.Overfitting may result in incorrectly labeled data or in inaccuratepredictions. An algorithm may experience underfitting when it fails todecipher the underlying trend in the input dataset because it does notfit the data well enough.

A machine learning betting system may measure error once the model istrained. New data may be fed to the model, and the outcome may bechecked and categorized into one of four types of results: truepositive, true negative false positive, and false negative. A truepositive result may be when the model predicts a condition when thecondition is present. A true negative result may be when the model doesnot predict a condition when it is absent. A false-positive result maybe when the model predicts a condition when it is absent. A falsenegative may be when the model does not predict a condition when it isabsent. The sum of false positives and false negatives may be the totalerror in the model. While an algorithm or hypothesis can fit well to atraining set, it might fail when applied to another data set outside thetraining set. Determining if the algorithm is fit for new data may beperformed by testing it with a set of new data. Generalization may referto how well the model predicts outcomes for a new set of data. Noisemust also be managed, and data parameters tested. A machine learningbetting system may go through several cycles of training, validation,and testing until the error in the model is brought within an acceptablerange.

A machine learning betting system may use one or more types of machinelearning. Supervised machine learning algorithms can use data that hasalready been analyzed, by a person or another algorithm, to classify newdata. Analyzing a known training dataset may allow a supervised machinelearning algorithm to produce an inferred function to predict outputvalues in the new data. As input data is fed into the model, it maychange the weighing of characteristics until the model is fittedappropriately. This supervised learning may be part of cross-validationwhich may ensure that the model avoids overfitting or underfitting.Supervised learning may help organizations solve various real-worldproblems at scale, such as classifying spam in a separate email folder.

Supervised machine learning algorithms may be adept at dividing datainto two categories (binary classification), choosing between more thantwo types of answers (multi-class classification), predicting continuousvalues (regression modeling), or combining the predictions of multiplemachine learning models to produce an accurate prediction (ensembling).Some methods used in supervised learning may include neural networks,naïve Bayes, linear regression, logistic regression, random forest,support vector machine (SVM), and more. A supervised machine learningbetting system may be provided a dataset of historical sporting events,the odds of various outcomes of those sporting events, and the actionwaged on those outcomes. It may use that data to predict the action onfuture outcomes by identifying similar historical outcomes. A machinelearning betting system may utilize recommendation algorithms to learnuser preferences for teams, players, sports, wagers, etc.

Unsupervised machine learning may analyze and cluster data that has notyet been analyzed to discover hidden patterns or groupings within thedata without the need for a human to define the patterns or groupings.The ability of unsupervised machine learning algorithms to discoversimilarities and differences in information may be an ideal solution forexploratory data analysis, cross-selling strategies, customersegmentation, image, and pattern recognition. Most types of deeplearning, including neural networks, may be unsupervised algorithms.

Unsupervised machine learning may be utilized in dimensionalityreduction or the process of reducing the number of random variablesunder consideration by identifying a set of principal variables.Unsupervised machine learning may split datasets into groups based onsimilarity, also known as clustering. It may also engage in anomalydetection by identifying unusual data points in a data set. It may alsoidentify items in a data set that frequently occur together, also knownas association mining. Principal component analysis and singular valuedecomposition are two methods of dimensionality reduction that may beemployed. Other algorithms used in unsupervised learning may includeneural networks, k-means clustering, probabilistic clustering methods,and more.

A machine learning betting system may fall between a supervised machinelearning algorithm and an unsupervised one. In these systems, analgorithm was trained with a smaller labeled dataset to identifyfeatures and classify a larger, unlabeled dataset. These types ofalgorithms may perform better when provided with labeled datasets.However, labeling can be time-consuming and expensive, which is whereunsupervised learning can provide efficiency benefits. For example, asportsbook may identify a cohort of users in a dataset who exhibitdesirable behavior. A semi-supervised machine learning betting systemmay be used to identify other users in the cohort who are desirable.

Reinforcement learning is when data scientists teach a machine learningalgorithm to complete a multi-step process with clearly defined rules.The algorithm is programmed to complete a task and is given positive andnegative feedback or cues as it works out how to complete the task ithas been given. The prescribed set of rules for accomplishing a distinctgoal may allow the algorithm to learn and decide which steps to takealong the way. This combination of rules along with positive andnegative feedback may allow a reinforcement learning machine learningbetting system to optimize the task over time. A machine learningbetting system may utilize reinforcement learning to identify potentialcheaters by recognizing a series of behaviors associated withundesirable player conduct, cheating, or fraud.

Some embodiments of this disclosure, illustrating all its features, willnow be discussed in detail. It can be understood that the embodimentsare intended to be open-ended in that an item or items used in theembodiments is not meant to be an exhaustive listing of such items oritems or meant to be limited to only the listed item or items.

It can be noted that as used herein and in the appended claims, thesingular forms “a,” “an,” and “the” include plural references unless thecontext clearly dictates otherwise. Although any systems and methodssimilar or equivalent to those described herein can be used in thepractice or testing of embodiments, only some exemplary systems andmethods are now described.

FIG. 1 is a system for wagering on event outcomes based on two timingsduring an event. This system may include a live event 102, for example,a sporting event such as a football, basketball, baseball, or hockeygame, tennis match, golf tournament, eSports, or digital game, etc. Thelive event 102 may include some number of actions or plays, upon which auser, bettor, or customer can place a bet or wager, typically through anentity called a sportsbook. There are numerous types of wagers thebettor can make, including, but not limited to, a straight bet, a moneyline bet, or a bet with a point spread or line that the bettor's teamwould need to cover if the result of the game with the same as the pointspread the user would not cover the spread, but instead the tie iscalled a push. If the user bets on the favorite, points are given to theopposing side, which is the underdog or longshot. Betting on allfavorites is referred to as chalk and is typically applied toround-robin or other tournaments' styles. There are other types ofwagers, including, but not limited to, parlays, teasers, and prop bets,which are added games that often allow the user to customize theirbetting by changing the odds and payouts received on a wager. Certainsportsbooks will allow the bettor to buy points which moves the pointspread off the opening line. This increases the price of the bet,sometimes by increasing the juice, vig, or hold that the sportsbooktakes. Another type of wager the bettor can make is an over/under, inwhich the user bets over or under a total for the live event 102, suchas the score of an American football game or the run line in a baseballgame, or a series of actions in the live event 102. Sportsbooks haveseveral bets they can handle, limiting the number of wagers they cantake on either side of a bet before they will move the line or odds offthe opening line. Additionally, there are circumstances, such as aninjury to an important player like a listed pitcher, in which asportsbook, casino, or racino may take an available wager off the board.As the line moves, an opportunity may arise for a bettor to bet on bothsides at different point spreads to middle, and win, both bets.Sportsbooks will often offer bets on portions of games, such asfirst-half bets and half-time bets. Additionally, the sportsbook canoffer futures bets on live events in the future. Sportsbooks need tooffer payment processing services to cash out customers which can bedone at kiosks at the live event 102 or at another location.

Further, embodiments may include a plurality of sensors 104 that may beused such as motion, temperature, or humidity sensors, optical sensors,and cameras such as an RGB-D camera which is a digital camera capable ofcapturing color (RGB) and depth information for every pixel in an image,microphones, radiofrequency receivers, thermal imagers, radar devices,lidar devices, ultrasound devices, speakers, wearable devices, etc.Also, the plurality of sensors 104 may include but are not limited to,tracking devices, such as RFID tags, GPS chips, or other such devicesembedded on uniforms, in equipment, in the field of play and boundariesof the field of play, or on other markers in the field of play. Imagingdevices may also be used as tracking devices, such as player tracking,which provide statistical information through real-time X, Y positioningof players and X, Y, Z positioning of the ball.

Further, embodiments may include a cloud 106 or a communication networkthat may be a wired and/or wireless network. The communication network,if wireless, may be implemented using communication techniques such asvisible light communication (VLC), worldwide interoperability formicrowave access (WiMAX), long term evolution (LTE), wireless local areanetwork (WLAN), infrared (IR) communication, public switched telephonenetwork (PSTN), radio waves, or other communication techniques that areknown in the art. The communication network may allow ubiquitous accessto shared pools of configurable system resources and higher-levelservices that can be rapidly provisioned with minimal management effort,often over the Internet, and relies on sharing resources to achievecoherence and economies of scale, like a public utility. In contrast,third-party clouds allow organizations to focus on their core businessesinstead of expending resources on computer infrastructure andmaintenance. The cloud 106 may be communicatively coupled to apeer-to-peer wagering network 114, which may perform real-time analysison the type of play and the result of the play. The cloud 106 may alsobe synchronized with game situational data such as the time of the game,the score, location on the field, weather conditions, and the like,which may affect the choice of play utilized. For example, in anexemplary embodiment, the cloud 106 may not receive data gathered fromthe sensors 104 and may, instead, receive data from an alternative datafeed, such as Sports Radar®. This data may be compiled substantiallyimmediately following the completion of any play and may be comparedwith a variety of team data and league data based on a variety ofelements, including the current down, possession, score, time, team, andso forth, as described in various exemplary embodiments herein.

Further, embodiments may include a mobile device 108 such as a computingdevice, laptop, smartphone, tablet, computer, smart speaker, or I/Odevices. I/O devices may be present in the computing device. Inputdevices may include but are not limited to, keyboards, mice, trackpads,trackballs, touchpads, touch mice, multi-touch touchpads and touch mice,microphones, multi-array microphones, drawing tablets, cameras,single-lens reflex cameras (SLRs), digital SLRs (DSLRs), complementarymetal-oxide semiconductor (CMOS) sensors, accelerometers, IR opticalsensors, pressure sensors, magnetometer sensors, angular rate sensors,depth sensors, proximity sensors, ambient light sensors, gyroscopicsensors, or other sensors. Output devices may include but are notlimited to, video displays, graphical displays, speakers, headphones,inkjet printers, laser printers, or 3D printers. Devices may include,but are not limited to, a combination of multiple input or outputdevices such as, Microsoft KINECT, Nintendo Wii remote, Nintendo WII UGAMEPAD, or Apple iPhone. Some devices allow gesture recognition inputsby combining input and output devices. Other devices allow for facialrecognition, which may be utilized as an input for different purposessuch as authentication or other commands. Some devices provide for voicerecognition and inputs including, but not limited to, Microsoft KINECT,SIRI for iPhone by Apple, Google Now, or Google Voice Search. Additionaluser devices have both input and output capabilities including but notlimited to, haptic feedback devices, touchscreen displays, ormulti-touch displays. Touchscreen, multi-touch displays, touchpads,touch mice, or other touch sensing devices may use differenttechnologies to sense touch, including but not limited to, capacitive,surface capacitive, projected capacitive touch (PCT), in-cellcapacitive, resistive, IR, waveguide, dispersive signal touch (DST),in-cell optical, surface acoustic wave (SAW), bending wave touch (BWT),or force-based sensing technologies. Some multi-touch devices may allowtwo or more contact points with the surface, allowing advancedfunctionality including, but not limited to, pinch, spread, rotate,scroll, or other gestures. Some touchscreen devices, including but notlimited to, Microsoft PIXELSENSE or Multi-Touch Collaboration Wall, mayhave larger surfaces, such as on a table-top or on a wall, and may alsointeract with other electronic devices. Some I/O devices, displaydevices, or groups of devices may be augmented reality devices. An I/Ocontroller may control one or more I/O devices, such as a keyboard and apointing device, or a mouse or optical pen. Furthermore, an I/O devicemay also contain storage and/or an installation medium for the computingdevice. In some embodiments, the computing device may include USBconnections (not shown) to receive handheld USB storage devices. Infurther embodiments, an I/O device may be a bridge between the systembus and an external communication bus, e.g., USB, SCSI, FireWire,Ethernet, Gigabit Ethernet, Fiber Channel, or Thunderbolt buses. In someembodiments, the mobile device 108 could be an optional component andmay be utilized in a situation where a paired wearable device employsthe mobile device 108 for additional memory or computing power orconnection to the internet.

Further, embodiments may include a wagering software application or awagering app 110, which is a program that enables the user to place betson individual plays in the live event 102, streams audio and video fromthe live event 102, and features the available wagers from the liveevent 102 on the mobile device 108. The wagering app 110 allows the userto interact with the wagering network 114 to place bets and providepayment/receive funds based on wager outcomes.

Further, embodiments may include a mobile device database 112 that maystore some or all the user's data, the live event 102, or the user'sinteraction with the wagering network 114.

Further, embodiments may include the wagering network 114, which mayperform real-time analysis on the type of play and the result of a playor action. The wagering network 114 (or the cloud 106) may also besynchronized with game situational data, such as the time of the game,the score, location on the field, weather conditions, and the like,which may affect the choice of play utilized. For example, in anexemplary embodiment, the wagering network 114 may not receive datagathered from the sensors 104 and may, instead, receive data from analternative data feed, such as SportsRadar®. This data may be providedsubstantially immediately following the completion of any play and maybe compared with a variety of team data and league data based on avariety of elements, including the current down, possession, score,time, team, and so forth, as described in various exemplary embodimentsherein. The wagering network 114 can offer several SaaS managed servicessuch as user interface service, risk management service, compliance,pricing and trading service, IT support of the technology platform,business applications, game configuration, state-based integration,fantasy sports connection, integration to allow the joining of socialmedia, or marketing support services that can deliver engagingpromotions to the user.

Further, embodiments may include a user database 116, which may containdata relevant to all users of the wagering network 114 and may include,but is not limited to, a user ID, a device identifier, a paired deviceidentifier, wagering history, or wallet information for the user. Theuser database 116 may also contain a list of user account recordsassociated with respective user IDs. For example, a user account recordmay include, but is not limited to, information such as user interests,user personal details such as age, mobile number, etc., previouslyplayed sporting events, highest wager, favorite sporting event, orcurrent user balance and standings. In addition, the user database 116may contain betting lines and search queries. The user database 116 maybe searched based on a search criterion received from the user. Eachbetting line may include but is not limited to, a plurality of bettingattributes such as at least one of the following: the live event 102, ateam, a player, an amount of wager, etc. The user database 116 mayinclude, but is not limited to, information related to all the usersinvolved in the live event 102. In one exemplary embodiment, the userdatabase 116 may include information for generating a user authenticityreport and a wagering verification report. Further, the user database116 may be used to store user statistics like, but not limited to, theretention period for a particular user, frequency of wagers placed by aparticular user, the average amount of wager placed by each user, etc.

Further, embodiments may include a historical plays database 118 thatmay contain play data for the type of sport being played in the liveevent 102. For example, in American Football, for optimal oddscalculation, the historical play data may include metadata about thehistorical plays, such as time, location, weather, previous plays,opponent, physiological data, etc.

Further, embodiments may utilize an odds database 120—that may containthe odds calculated by an odds calculation module 122—to display theodds on the user's mobile device 108 and take bets from the user throughthe mobile device wagering app 110.

Further, embodiments may include the odds calculation module 122, whichmay utilize historical play data to calculate odds for in-play wagers.For example, the odds calculation module 122 may continuously poll forthe data from the live event 102. The odds calculation module 122 mayreceive the data from the live event 102. The odds calculation module122 may store the results data, or the results of the last action, inthe historical play database 118, which may contain historical data ofall previous actions. The odds calculation module 122 may filter thehistorical play database 118 on the team and down from the situationaldata. The first parameter of the historical plays database 118 may beselected, for example, the event. Then the odds calculation module 122may perform correlations on the data. For example, the historical actiondatabase 130 may be filtered on the team, the players, the quarter, thedown, and the distance to be gained. The first parameter may beselected, which in this example, the event, which may either be a passor a run and the historical action database 130 may be filtered on theevent being a pass. Then, correlations may be performed on the rest ofthe parameters: yards gained, temperature, decibel level, etc. In FIG.3B, the graph may show the correlated data for the historical datainvolving the Patriots in the second quarter on second down with fiveyards to go and the action being a pass, which has a correlationcoefficient of 0.81. The correlations may also be performed with thesame filters and the next event, which is the action being a run shownin FIG. 3B and has a correlation coefficient of 0.79. It may bedetermined if the correlation coefficient is above a predeterminedthreshold, for example, 0.75, to determine if the data is highlycorrelated and deemed a relevant correlation. If the correlation isdeemed highly relevant, then the correlation coefficient is extractedfrom the date. For example, the two correlation coefficients of 0.81 fora pass and 0.79 for a run are both extracted. If the correlations arenot highly relevant, then then it may be determined if any parametersare remaining. Also, if the correlations were determined to be highlyrelevant, it may be determined if any parameters are remaining toperform correlations on. If there are additional parameters to havecorrelations performed, then the odds calculation module 122 may selectthe next parameter in the historic action database and return toperforming correlations on the data. Once there are no remainingparameters to perform correlations on, the odds calculation module 122may determine the difference between each of the extracted correlations.For example, the correlation coefficient for a pass is 0.81, and thecorrelation coefficient for a run is 0.79. The difference between thetwo correlation coefficients (0.81−0.79) is 0.02. In some embodiments,the difference may be calculated by using subtraction on the twocorrelation coefficients. In some embodiments, the two correlationcoefficients may be compared by determining the statisticalsignificance. The statistical significance, in an embodiment, can bedetermined by using the following formula: Zobserved=(z1−z2)/(squareroot of [(1 /N1−3)+(1 /N2−3)], where z1 is the correlation coefficientof the first dataset, z2 is the correlation coefficient of the seconddataset, N1 is the sample size of the first dataset, and N2 is thesample size of the second dataset, and the resulting Zobserved may beused instead of the difference of the correlation coefficients in arecommendation database to compare the two correlation coefficient basedon statistical significance as opposed to the difference of the twocorrelation coefficients. The difference between the two correlationcoefficients, 0.02, may then be compared to the recommendation database.The recommendation database may contain various ranges of differences incorrelations as well as the corresponding odds adjustment for thoseranges. For example, the 0.02 difference of the two correlationcoefficients may fall into the range +0−2 difference in correlations,which should have an odds adjustment of a 5% increase according to therecommendation database. The odds calculation module 122 may thenextract the odds adjustment from the recommendation database. Theextracted odds adjustment may be stored in an adjustment database. Theodds calculation module 122 may compare the odds database 120 to theadjustment database. It may be determined whether there is a match inany of the wager IDs in the odds database 120 and the adjustmentdatabase. For example, the odds database 120 may contain all the currentbet options for a user. The odds database 120 may contain a wager ID,event, time, quarter, wager, and odds for each bet option. Theadjustment database may contain the wager ID and the percentage, eitheras an increase or decrease, that the odds should be adjusted. If thereis a match between the odds database 120 and the adjustment database,then the odds in the odds database 120 may be adjusted by the percentageincrease or decrease in the adjustment database, and the odds in theodds database 120 may be updated. For example, if the odds in the oddsdatabase 120 are −105 and the matched wager ID in the adjustmentdatabase is a 5% increase, then the updated odds in the odds database120 should be −110. If there is a match, the odds may be adjusted basedon the data stored in the adjustment database, and the new data may bestored in the odds database 120 over the old entry. If there are nomatches, or, once the odds database 120 134 has been adjusted if thereare matches, the odds calculation module 122 may offer the odds database120 to the wagering app 110, allowing users to place bets on the wagersstored in the odds database 120. In other embodiments, it may beappreciated that the previous formula may be varied depending on avariety of reasons, for example, adjusting odds based on further factorsor wagers, adjusting odds based on changing conditions or additionalvariables, or based on a desire to change wagering action. Additionally,in other example embodiments, one or more alternative equations may beutilized in the odds calculation module 122. One such equation could beZobserved=(z1−z2)/(square root of [(1 /N1−3)+(1 /N2−3)], where z1 is thecorrelation coefficient of the first dataset, z2 is the correlationcoefficient of the second dataset, N1 is the sample size of the firstdataset, and N2 is the sample size of the second dataset, and theresulting Zobserved to compare the two correlation coefficient based onstatistical significance as opposed to the difference of the twocorrelation coefficients. Another equation used may be Z=b1−b2/Sb1−b2 tocompare the slopes of the datasets or may introduce any of a variety ofadditional variables, such as b1 is the slope of the first dataset, b2is the slope for the second dataset, Sb1 is the standard error for theslope of the first dataset and Sb2 is the standard error for the slopeof the second dataset. The results of calculations made by suchequations may then be compared to the recommendation data, and the oddscalculation module 122 may then extract an odds adjustment from therecommendation database. The extracted odds adjustment may be stored inthe adjustment database. In some embodiments, the recommendationsdatabase may be used in the odds calculation module 122 to determine howthe wager odds should be adjusted depending on the difference betweenthe correlation coefficients of the correlated data points. Therecommendations database may contain the difference in correlations andthe odds adjustment. For example, in FIG. 3B there is a correlationcoefficient for a Patriots 2nd down pass of 0.81 and a correlationcoefficient for a Patriots 2nd run of 0.79, the difference between thetwo may be +0.02 when compared to the recommendation database the oddsadjustment may be a 5% increase for a Patriots pass or otherwiseidentified as wager 201 in the adjustment database. In some embodiments,the difference in correlations may be the statistical significance ofcomparing the two correlation coefficients to determine how the oddsshould be adjusted. In some embodiments, the adjustment database may beused to adjust the wager odds of the odds database 120 If a wager shouldbe adjusted. The adjustment database may contain the wager ID, which isused to match with the odds database 120 to adjust the odds of thecorrect wager.

Further, embodiments may include the state identification module 124,which may identify or otherwise define a state of an event. The statemay comprise a particular status of a game, e.g., the players on thefield, the count of the pitch, the inning, and other information. Insome embodiments, several possible future states may be identified, andprobability and odds may be determined for each possible future state.For example, in baseball, a state may be identified based on thefollowing factors: number of outs, number of balls and strikes of thecurrent batter, number of players on base, and the bases occupied. Theinning may further identify the state, score, pitcher identity, batteridentity, identities of fielders and any players on base, next batter(s)up, temperature, weather conditions, etc. Possible future states, forexample, after a given pitch or at-bat, may be identified according toall the possible outcomes of the pitch, such as several runs, newplayers' positions on base, another out, etc. The state identificationmodule 124 may also update the state of an event based on eventinformation received from the live event 102. For instance, the stateidentification module 124 may receive event information such as a videostream of an event or a text play-by-play of an event. The stateidentification module 124 may process the event information. Forinstance, the state identification module 124 may process video or otherimage information to determine information about the state of an eventsuch as a game, e.g., the location of a football at the end of a playwhen a referee signals the end of the play, whether a football carriercrossed a first down marker, whether a player stepped out of bounds,whether a ball hit by a batter was hit out of the park within the “fair”territory, whether a tennis ball hits the net or lands in “out”territory, etc. Accordingly, the state identification module 124 mayautomatically determine information related to performance parametersand outcomes, for example, whether a batter was struck out or whether aquarterback threw an interception. In some embodiments, stateidentification module 124 may reconcile identified states with anothersource of information related to an event. For instance, in someembodiments, the sensors 104, a gaming agent, etc., may interact withstate identification module 124 to define states, correct any errors inautomatically determined states, and provide additional eventinformation. For instance, the sensors 104, a gaming agent, etc., maynote that a flag was called on a play after state identification module124 may determine that a touchdown was scored during the play. In someembodiments, the state identification module 124 may reconcileinformation determined about a state with an “official” source of stateinformation. For instance, an official umpire who referees a game may bean “official” source of information, such that a call made by a refereeconcerning whether a ball was a ball may be controlling regardless ofwhether the state identification module 124 may determine that a ballcrossed a batter's strike zone. Accordingly, the state identificationmodule 124 may interact with the umpire to determine official “calls” inthe game, and this information may be used to update the event's state.In some embodiments, referees may enter state information, such as“ball” or “strike,” on a device that communicates directly with thestate identification module 124 so that the state identification module124 may update state information accurately. Information about statesmay be communicated to the wagering network 114, wagering app 110, orusers. It may also be used to determine initial states and possibleoutcomes of a performance parameter and to determine or updateprobabilities associated with the outcomes. In some embodiments, thestate identification module 124 may automatically identify event statesand state changes, such as betting outcomes. For instance, the stateidentification module 124 may analyze one or more data sources toautomatically identify an event state, such as an outcome of a bettingevent. For instance, the state identification module 124 may use avariety of sources to determine and/or confirm that a tennis player hasscored a point, such as image processing software that analyzes a livevideo feed of the match; “play-by-play” information from a data feed;and a website that outputs the score of a game in substantiallyreal-time. The state identification module 124 may analyze a video feedof a live tennis match to identify whether a tennis ball landed “in” or“out” on a player's side of the court. The state identification module124 may also review “play-by-play” information from a data feedindicating that a tennis point has been scored by the player. In someembodiments, a human operator may determine the outcome of a bettingevent. For instance, the human operator may watch the live event, forexample, live in person or via live television broadcast. In someembodiments, a human operator may determine the outcome of a bettingevent and then cause the system to settle bets based on the determinedoutcome. In this way, the bettors will have immediate feedback.

Further, embodiments may include a base module 128, which may receivepre-event information about an event. The base module 128 may send thepre-event information to one or more users. The base module 128 mayreceive information about the event during the event. Then the basemodule 128 may send the game information to one or more users. The basemodule 128 may define a performance parameter. For example, theperformance parameter may be created by the parameter creation module126. The performance parameter may be created based on the system'sperformance categories, variables, metrics, and other event orperformance information. For example, the parameter creation module 126may allow the system to define a performance parameter or a plurality ofperformance parameters. The base module 128 may determine the stateinformation relating to a performance parameter. The state informationmay comprise any status or historical information about the event. Insome embodiments, the state identification module 124 may determine thestate information relating to a performance parameter. The base module128 may identify a first timing associated with a set of possible futurestates associated with a performance parameter. The base module 128 maydetermine statistics associated with an outcome or type of outcome. Thebase module 128 may determine the probabilities and odds associated withone or more possible outcomes. In some embodiments, the probabilitiesmay be received or calculated from a database of statistical informationor probabilities. A third party may maintain the database. In someembodiments, the probabilities and odds associated with one or morepossible outcomes may be determined by the odds calculation module 122.Then a user may request information relevant to a wager from the basemodule 128. The base module 128 may provide wager information. A usermay select a wager. The user may confirm a wager. The base module 128may close wagers for a particular event or wagering market, such asafter a first timing for a particular in-game event and after the secondtiming for a particular in-game event has been completed. The basemodule 128 may receive real-time information about the state of anevent. The base module 128 may identify an outcome of an event, such asan event that is the subject of a wager. For example, the base module128 may determine an actual state among the possible future states. Forexample, the base module 128 may determine that the New England Patriotsoffensive drive resulted in a touchdown based on a source of eventinformation. The base module 128 may then resolve all bets related tothe associated wagering market. For example, the base module 128 mayissue an appropriate payout to any winning betters and issuenotifications to those who had losing bets. In some embodiments, thewagering network 114 may close, payout, and notify the users of winningwagers. In some embodiments, users may keep a wagering account that maycontain an amount for wagering. Users may continue making wagers untilthe account is depleted or otherwise unavailable for further wagers.

FIG. 2 illustrates the base module 128. The process may begin with thebase module 128 receiving, at step 200, pre-event information about anevent. For example, the base module 128 may receive from a data feedinformation about two teams playing in a game, such as a team roster,batting line-up (baseball), starting offensive line (football), aninjury report for any potentially injured players, a game start time,and other information. The information may be downloaded or otherwisereceived from any source, such as a league or team website, pressrelease, ESPN™, SportsRadar, or other sources of event information. Thebase module 128 may send, at step 202, the pre-event information to thewagering app and one or more users. For example, the game information,such as live video footage of a sporting event, may be displayed to theuser on a television or computer monitor, radio, mobile phone, or otheroutput devices. In some embodiments, the information may be displayed ona device capable of receiving user inputs, such as a mobile device 108,smartphone, touch-sensitive display device, etc. The display device maybe operable to accept user inputs, such as selecting various bettingoptions on a touch-sensitive display. The base module 128 may receive,at step 204, information about the event during the event. For example,the system may receive a data feed or a live broadcast. Then the basemodule 128 may send, at step 206, the game information to the wageringapp and one or more users_([A1]). For example, the information may besent to users and gaming agents. For example, the game information, suchas live video footage of a sporting event, may be displayed to the useron a television or computer monitor, radio, mobile phone, or otheroutput devices. In some embodiments, the information may be displayed ona device capable of receiving user inputs, such as a mobile device 108,smartphone, touch-sensitive display device, etc. The display device maybe operable to accept user inputs, such as selecting from among variousbetting options on a touch-sensitive display. The base module 128 maydefine, at step 208, a performance parameter. For example, theperformance parameter may be created by the parameter creation module126. The performance parameter may be created based on the system'sperformance categories, variables, metrics, and other event orperformance information. For example, the parameter creation module 126may allow the system to define a performance parameter or a plurality ofperformance parameters. For example, the performance parameter may bethe number of batters that pitcher Chris Sale strikes out in the secondinning of the Boston Red Sox vs. New York Yankees event, the number ofjump shots that Kevin Durant attempts in the third quarter of theBrooklyn Nets vs. the Toronto Raptors event, the number of saves thatTuukka Rask will have in the last ten minutes of the second period inthe Boston Bruins vs. New York Rangers event, the number of passingyards Patrick Mahomes will have in the first offensive drive in thethird quarter in the Kansas City Chiefs vs. Las Vegas Raiders event,etc. The system may have predefined performance parameters that areselected by the system and offered to the users through the wagering app110 to allow the users to place wagers. The parameter creation module126 may select the predefined performance parameters through acombination of selecting the wager type and determining if the selectedwager type has sufficient data to determine wager odds and determine ifthe wager if offered, would have users place a wager on the wager. Forexample, the parameter creation module 126 may select a first wagertype, such as the number of pitches thrown in an inning for a pitcher inan event. Then the parameter creation module 126, through the processdescribed in the odds calculation module 122, may determine if theselected wager type would have sufficient data by filtering thehistorical plays database 118 on the parameters of the wager, such asthe pitcher, the inning, the opponent, etc. If sufficient data remainsin the historical plays database 118, then the parameter creation module126 may determine if users would place a wager on the wager if it wereoffered on the wagering app 110 through the wagering network 114. Forexample, the parameter creation module 126 may determine if a user wouldplace a wager on the potentially offered wager by determining if it is apopular market, such as how many users have placed a wager on a similarwager type in the past by filtering the user database 116 on the wagertype and if a predetermined percentage of the total number of users haveplaced a wager on the wager type then the wager may be offered on thewagering app 110 through the wagering network 114. In some embodiments,the wager type may result from a single play in American football games,baseball games, basketball games, etc. For example, if the offensivefootball team will run, pass, gain a predetermined number of yards,score a touchdown, turnover the football, such as a fumble orinterception, make or miss a field goal, gain a first down, etc. if thedefensive football team will stop the offensive team for negativeyardage, force a turnover such as a fumble or interception, sack thequarterback, allow a first down, touchdown, field, prevent a first down,touchdown, field goal, etc. For example, in baseball, the result of thebatter such as a single, double, triple, home run, strikeout, groundout,flyout, the location of a hit and the result of the hit, a stolen base,etc., the result of the pitcher, such as a strike, ball, strikeout,walk, allow a hit, etc., the result of the fielders such as an error,double play, a runner caught stealing, etc. In some embodiments, thedetermination of the parameter creation module 126 of determining if theusers will place a wager on the selected wager type may be accomplishedby the overall money wagered on the wager type, the number of users inthe past wagering on the wager type, the overall revenue to the wageringnetwork, etc. The base module 128 may determine, at step 210, the stateinformation relating to a performance parameter. The state informationmay comprise any status or historical information about the event. Insome embodiments, the state identification module 124 may determine thestate information relating to a performance parameter. For example,based on a real-time data feed of a sporting event, the stateidentification module 124 may determine whether a sporting event hasbegun, the score, time remaining, balls and strikes of a specificbatter, which players are currently on the field, and other stateinformation described herein. The base module 128 may identify, at step212, a plurality of possible future states of the event. The end statesmay (or may not) be mutually exclusive, and they may relate to aspecific performance parameter or occurrence that happens during theevent. The base module 128 may identify, at step 214, a first timingassociated with a set of possible future states associated with aperformance parameter. For instance, a first timing may be the beginningof an offensive drive for a football team, and the second timing may bewhen the offensive drive is completed, finished, or has a result, suchas a touchdown, field goal, turnover, etc. The base module 128 maydetermine, at step 216, statistics associated with an outcome or type ofoutcome. For instance, the system may determine statistics associatedwith one or more performance parameters associated with a particularteam or player (or other event entity), such as whether a particularplayer will get a single or a home run for a particular at-bat. The basemodule 128 may determine, at step 218, the probabilities and oddsassociated with one or more possible outcomes. In some embodiments, theprobabilities may be received or calculated from a database ofstatistical information or probabilities. A third party may maintain thedatabase. In some embodiments, the probabilities and odds associatedwith one or more possible outcomes may be determined by the oddscalculation module 122. For example, the odds calculation module 122 mayreceive the data from the live event 102. The odds calculation module122 may store the results data, or the results of the last action, inthe historical play database 118, which may contain historical data ofall previous actions. The odds calculation module 122 may filter thehistorical play database 118 on the team and down from the situationaldata. The first parameter of the historical plays database 118 may beselected, for example, the event. Then the odds calculation module 122may perform correlations on the data. For example, the historical actiondatabase 130 may be filtered on the team, the players, the quarter, thedown, and the distance to be gained. The first parameter may beselected, which in this example, the event, which may either be a passor a run and the historical action database 130 may be filtered on theevent being a pass. Then, correlations may be performed on the rest ofthe parameters: yards gained, temperature, decibel level, etc. In FIG.3B, the graph may show the correlated data for the historical datainvolving the Patriots in the second quarter on second down with fiveyards to go and the action being a pass, which has a correlationcoefficient of 0.81. The correlations may also be performed with thesame filters and the next event, which is the action being a run whichis also shown in FIG. 3B and has a correlation coefficient of 0.79. Itmay be determined if the correlation coefficient is above apredetermined threshold, for example, 0.75, to determine if the data ishighly correlated and deemed a relevant correlation. If the correlationis deemed highly relevant, then the correlation coefficient is extractedfrom the date. For example, the two correlation coefficients of 0.81 fora pass and 0.79 for a run are both extracted. If the correlations arenot highly relevant, then then it may be determined if any parametersare remaining. Also, if the correlations were determined to be highlyrelevant, it is also determined if any parameters are remaining toperform correlations on. If there are additional parameters to havecorrelations performed, then the odds calculation module 122 may selectthe next parameter in the historic action database and returns toperforming correlations on the data. Once there are no remainingparameters to perform correlations on, the odds calculation module 122may determine the difference between each of the extracted correlations.For example, the correlation coefficient for a pass is 0.81, and thecorrelation coefficient for a run is 0.79. The difference between thetwo correlation coefficients (0.81−0.79) is 0.02. In some embodiments,the difference may be calculated by using subtraction on the twocorrelation coefficients. In some embodiments, the two correlationcoefficients may be compared by determining the statisticalsignificance. The statistical significance, in an embodiment, can bedetermined by using the following formula: Zobserved=(z1−z2)/(squareroot of [(1 /N1−3)+(1 /N2−3)], where z1 is the correlation coefficientof the first dataset, z2 is the correlation coefficient of the seconddataset, N1 is the sample size of the first dataset, and N2 is thesample size of the second dataset, and the resulting Zobserved may beused instead of the difference of the correlation coefficients in arecommendation database to compare the two correlation coefficient basedon statistical significance as opposed to the difference of the twocorrelation coefficients. The difference between the two correlationcoefficients, 0.02, may then be compared to the recommendation database.The recommendation database may contain various ranges of differences incorrelations as well as the corresponding odds adjustment for thoseranges. For example, the 0.02 difference of the two correlationcoefficients fall into the range+0−2 difference in correlations, whichshould have an odds adjustment of 5% increase according to therecommendation database. The odds calculation module 122 may thenextract the odds adjustment from the recommendation database. Theextracted odds adjustment may be stored in an adjustment database. Theodds calculation module 122 may compare the odds database 120 to theadjustment database. It may be determined whether there is a match inany of the wager IDs in the odds database 120 and the adjustmentdatabase. For example, the odds database 120 may contain a list of allthe current bet options for a user. The odds database 120 may contain awager ID, event, time, quarter, wager, and odds for each bet option. Theadjustment database may contain the wager ID and the percentage, eitheras an increase or decrease, that the odds should be adjusted. If thereis a match between the odds database 120 and the adjustment database,then the odds in the odds database 120 may be adjusted by the percentageincrease or decrease in the adjustment database, and the odds in theodds database 120 may be updated. For example, if the odds in the oddsdatabase 120 are −105 and the matched wager ID in the adjustmentdatabase is a 5% increase, then the updated odds in the odds database120 should be −110. If there is a match, the odds may be adjusted basedon the data stored in the adjustment database, and the new data may bestored in the odds database 120 over the old entry. If there are nomatches, or, once the odds database 120 134 has been adjusted if thereare matches, the odds calculation module 122 may offer the odds database120 to the wagering app 110, allowing users to place bets on the wagersstored in the odds database 120.

In other embodiments, it may be appreciated that the previous formulamay be varied depending on a variety of reasons, for example, adjustingodds based on further factors or wagers, adjusting odds based onchanging conditions or additional variables, or based on a desire tochange wagering action. Additionally, in other example embodiments, oneor more alternative equations may be utilized in the odds calculationmodule 122. One such equation could be Zobserved=(z1−z2)/(square root of[(1/N1−3)+(1 /N2−3)], where z1 is the correlation coefficient of thefirst dataset, z2 is the correlation coefficient of the second dataset,N1 is the sample size of the first dataset, and N2 is the sample size ofthe second dataset, and the resulting Zobserved to compare the twocorrelation coefficient based on statistical significance as opposed tothe difference of the two correlation coefficients. Another equationused may be Z=b1−b2/Sb1−b2 to compare the slopes of the datasets or mayintroduce any of a variety of additional variables, such as b1 is theslope of the first dataset, b2 is the slope for the second dataset, Sb1is the standard error for the slope of the first dataset and Sb2 is thestandard error for the slope of the second dataset. The results ofcalculations made by such equations may then be compared to therecommendation data, and the odds calculation module 122 may thenextract an odds adjustment from the recommendation database. Theextracted odds adjustment may be stored in the adjustment database.

In some embodiments, the recommendations database may be used in theodds calculation module 122 to determine how the wager odds should beadjusted depending on the difference between the correlationcoefficients of the correlated data points. The recommendations databasemay contain the difference in correlations and the odds adjustment. Forexample, in FIG. 3B there is a correlation coefficient for a Patriots2nd down pass of 0.81 and a correlation coefficient for a Patriots 2ndrun of 0.79, the difference between the two may be +0.02 when comparedto the recommendation database the odds adjustment may be a 5% increasefor a Patriots pass or otherwise identified as wager 201 in theadjustment database. In some embodiments, the difference in correlationsmay be the statistical significance of comparing the two correlationcoefficients to determine how the odds should be adjusted.

In some embodiments, the adjustment database may be used to adjust thewager odds of the odds database 120 If a wager should be adjusted. Theadjustment database may contain the wager ID, which is used to matchwith the odds database 120 to adjust the odds of the correct wager. Auser may request, at step 220, information relevant to a wager from thebase module 128. For example, the user may request historicalinformation, such as information about a player or team's pastperformance, such as with respect to a particular performance parameterlike total yards and/or current performance, such as performance duringthe current game. The user may also request information about what betsare available and the odds for each. In some embodiments, a user mayrequest information by selecting a particular player on a screen. Forexample, a user may touch a video image of a pitcher to requestinformation about the pitcher's pitching record. In some embodiments, auser may select to bet that a pitcher will strike out a current batterby touching the image of the pitcher. The base module 128 may provide,at step 222, wager information. For example, the system may send a wageroverlay at a user display device. The overlay may display wager optionssuch as the various possible outcomes for which the user may wager, theodds for each, probabilities or statistics relating to the variousoutcomes, and any other information that may be relevant to a userwager. The user may select, at step 224, a wager. For example, the usermay navigate a menu of wager options, such as a touch-sensitive displayon a mobile phone. In some embodiments, a user may select the oppositeside of a wager proposed by another user. The user may confirm, at step226, a wager. For example, the user may wager that a particular batterwill strikeout. A user's wager may be placed automatically or canceledin some embodiments, such as based on triggering conditions. The basemodule 128 may close, at step 228, wagers for a particular event orwagering market, such as after a first timing for a particular in-gameevent and after the second timing for a particular in-game event hasbeen completed. For instance, the base module 128 may close wagering ona particular at-bat for J.D. Martinez before Martinez steps up to theplate to receive the first pitch, or after Martinez steps up to theplate but before the pitcher begins to throw the first pitch toMartinez. In some embodiments, the base module 128 may close wagersautomatically, such as without human intervention. In some embodiments,a human operator may close the wagers. For example, the human operatorwho is watching the event, such as live-in person or via TV broadcastwith minimal time delay, may cause the base module 128 to close wagersfor a particular at-bat now immediately before the pitcher throws thefirst pitch, or at another appropriate moment. In circumstances wherethe base module 128 does not receive information about an occurrence,such as a batter stepping up to the plate, until after occurrence hastaken place, for example, due to a 2-second delay in a televisionbroadcast, a human operator may be in a better position to determine anoptimal time for closing wagering for an event, such as the lastpossible moment before the beginning of the event. In some embodiments,the base module 128 may continue to allow wagers on the event, but thebetting options may change during the event. For example, the basemodule 128 may continuously or periodically update the odds of variousoutcomes based on current in-game information. For example, the basemodule 128 may recalculate the odds of various at-bat outcomes aftereach pitch through the process described in the odds calculation module122. For example, the base module 128 may determine that the odds that abatter strikes out increases after each strike, and the odds that thebatter will get walked increase after each resulting pitch is a ball. Insome embodiments, the base module 128 may hedge its exposure to one ormore outcomes. The base module 128 may receive, at step 230, real-timeinformation about the state of an event. The information may be receivedfrom any source of current event information, as discussed above. Thereal-time event state information may comprise real-time historicalinformation about the event, such as information relating to the resultof an event, such as a strikeout, information that might affect theprobability of an outcome of an event, such as a mild injury to aquarterback, performance information, such as, number of yards gained bya particular running back during a particular play, the game timeelapsed and remaining, and other event information. The base module 128may identify, at step 232, an outcome of an event, such as an event thatis the subject of a wager. For example, the base module 128 maydetermine an actual state among the possible future states. For example,the base module 128 may determine that the New England Patriotsoffensive drive resulted in a touchdown based on a source of eventinformation. The base module 128 may then resolve all bets related tothe associated wagering market. For example, the base module 128 mayissue an appropriate payout to any winning betters and issuenotifications to those who had losing bets. In some embodiments, thewagering network 114 may close, payout, and notify the users of winningwagers. In some embodiments, users may keep a wagering account that maycontain an amount for wagering. Users may continue making wagers untilthe account is depleted or otherwise unavailable for further wagers.

FIG. 3A provides an illustration of an example of the odds calculationmodule 122 and the resulting correlations. In FIG. 3A, the data may befiltered by the team, down and quarter, and finding the variouscorrelations with the team, down and quarter, and the various parameterssuch as the yards to gain, punt yardage, field goal yardage, etc. Anexample of non-correlated parameters with the team, down, and quarterand the yards to gain and punt yardage with a 15% (which is below the75% threshold), therefore there is no correlation, and the nextparameters may be correlated, unless there are no more parametersremaining.

FIG. 3B provides an illustration of an example of the odds calculationmodule 122 and the resulting correlations. In FIG. 3B, the data may befiltered by the team, down and quarter, and finding the variouscorrelations with the team, down and quarter and the various parameterssuch as the event, yards to gain, yards gained, etc. An example ofcorrelated parameters may be the live event 102 being a pass and theteam, down, and quarter with an 81%, therefore there may be acorrelation (since it is above the 75% threshold). The correlationcoefficient may need to be extracted and compared with the otherextracted correlation coefficient, which in this example is the eventdata where the event is a run, which is correlated at 79%. Thedifference between the two correlations may be compared to therecommendations database to determine if there is a need to adjust theodds. In this example, there is a 0.02 difference between the eventbeing a pass and the event being a run, which means on second down inthe second quarter, the New England Patriots are slightly more likely tothrow a pass than to run the ball, and the odds may be adjusted 5%decrease to match the correlated data. Conversely, if the correlateddata of run, 0.79, is compared to the correlated data of a pass, 0.81,the difference may be −0.02, and the odds may be adjusted by a 5%increase.

FIG. 4A provides an illustration for another example of the oddscalculation module 122 and the resulting correlations. In FIG. 4A, thedata that may be filtered by the team, down and quarter and finding thevarious correlations with the team, down and quarter and the variousparameters such as the decibel level in the stadium, punt yardage, fieldgoal yardage, etc. An example of non-correlated parameters with theteam, down, and quarter and the decibel level in the stadium and puntyardage with a 17% (which is below the 75% threshold), therefore thereis no correlation, and the next parameters may be correlated, unlessthere are no more parameters remaining.

FIG. 4B provides an illustration for another example of the oddscalculation module 122 and the resulting correlations. In FIG. 4B, thedata that may be filtered by the team, down and quarter and finding thevarious correlations with the team, down and quarter and the variousparameters such as the event, temperature, yards gained, etc. An exampleof correlated parameters is with the event being a run and the team,down, and quarter with a 92%, therefore there is a correlation (since itis above the 75% threshold). The correlation coefficient may need to beextracted and compared with the other extracted correlation coefficient,which in this example is the event data where the event is a pass, whichis correlated at 84%. The difference between the two correlations may becompared to the recommendations database to determine if there is a needto adjust the odds. In this example, there is a 0.08 difference betweenthe event being a run and the event being a pass, which means on firstdown in the first quarter the New England Patriots are more likely tothrow a run than to pass the ball, and the odds may be adjusted 15%decrease to match the correlated data. Conversely, if the correlateddata of run, 0.84, is compared to the correlated data of a pass, 0.92,then the difference may be −0.08, and the odds may be adjusted by a 15%increase.

The foregoing description and accompanying figures illustrate theprinciples, preferred embodiments, and modes of operation of theinvention. However, the invention should not be construed as beinglimited to the embodiments discussed above. Additional variations of theembodiments discussed above will be appreciated by those skilled in theart.

Therefore, the above-described embodiments should be regarded asillustrative rather than restrictive. Accordingly, it should beappreciated that variations to those embodiments can be made by thoseskilled in the art without departing from the scope of the invention asdefined by the following claims.

What is claimed is:
 1. A system for wagering on event outcomes usingseparate timings, comprising: at least one processor; and at least onememory having instructions stored thereon executed by the at least oneprocessor to: receive and send, to a wagering application, at least onepre-event information regarding a live event; define at least oneperformance parameter; determine at least one state informationregarding the performance parameter; identify at least one plurality ofpossible future states regarding the live event; identify at least onefirst timing and second timing associated with the plurality of possiblefuture states regarding the performance parameter; identify at least onestatistic regarding at least one type of outcome; determine at least oneof a probability and a set of odds associated with the type of outcomebetween the at least one of the at least one first timing and the secondtiming; and receive at least one information request from the wageringapplication and send at least one information request to the wageringapplication.
 2. The system for wagering on event outcomes using separatetimings of claim 1, further comprising receiving at least one wagerselection and at least one wager confirmation from the wageringapplication.
 3. The system for wagering on event outcomes using separatetimings of claim 1, wherein the processor further receives at least onestate of event information and identifies at least one outcome of thelive event.
 4. The system for wagering on event outcomes using separatetimings of claim 1, wherein the performance parameter is at least one ofa system category, system variable, and a system metric.
 5. The systemfor wagering on event outcomes using separate timings of claim 1,wherein the state information is at least one of a status or historicalinformation regarding the performance parameter.
 6. The system forwagering on event outcomes using separate timings of claim 1, whereinthe plurality of future states is at least one of a status or outcome ofthe live event.
 7. A betting exchange system for wagering on eventoutcomes using separate timings, comprising: at least one processor; andat least one memory having instructions stored thereon which, whenexecuted by the at least one processor, direct the at least oneprocessor to: receive and send, to a wagering application, at least onepre-event information regarding a live event; define at least oneperformance parameter; determine at least one state informationregarding the performance parameter; identify one or more of a pluralityof possible future states regarding the live event; identify at leastone first timing and second timing associated with the plurality ofpossible future states regarding the performance parameter; identify atleast one statistic regarding at least one type of outcome; determine atleast of a probability and a set of odds associated with the type ofoutcome; display information regarding at least one opportunity forplacement of at least one back bet or at least one lay bet; and receiveat least one information request from the wagering application and sendat least one information request to the wagering application.
 8. Thebetting exchange system for wagering on event outcomes using separatetimings of claim 7, further comprising receiving, from the wageringapplication, at least one of a back wager selection or lay wagerselection.
 9. The betting exchange system for wagering on event outcomesusing separate timings of claim 7, further comprising receiving at leastone wager selection and at least one wager confirmation from thewagering application and issuing a payout from at least one first cohortof users to at least one second cohort of users.
 10. A machine learningbetting system for wagering on event outcomes using separate timings,comprising: at least one processor; and at least one memory havinginstructions stored thereon which, when executed by the at least oneprocessor, direct the at least one processor to: receive and send, to awagering application, at least one pre-event information regarding alive event; define, with machine learning, at least one performanceparameter; determine, with machine learning, at least one stateinformation regarding the performance parameter; identify at least oneplurality of possible future states regarding the live event; identifyat least one first timing and second timing associated with theplurality of possible future states regarding the performance parameter;identify, with machine learning at least one statistic regarding atleast one type of outcome; determine, with machine learning at least oneset of probability and odds associated with the type of outcome; andreceive at least one information request from the wagering applicationand send at least one information request to the wagering application.11. The machine learning betting system for wagering on event outcomesusing separate timings of claim 10, wherein the machine learning is anunsupervised algorithm provided with at least one data set of historicalplay data for a live event.
 12. The machine learning betting system forwagering on event outcomes using separate timings of claim 10, whereinthe machine learning further comprises identifying at least one patternin at least one sequence of historical plays.
 13. The machine learningbetting system for wagering on event outcomes using separate timings ofclaim 10, further comprising receiving at least one wager selection andat least one wager confirmation from the wagering application.
 14. Amethod for wagering on event outcomes using separate timings,comprising: receiving and sending at least one pre-event information toa wagering application; defining at least one performance parameter;determining at least one state information regarding the performanceparameter; identifying at least one plurality of possible future statesregarding the live event; identifying at least one first timing andsecond timing associated with the plurality of possible future statesregarding the performance parameter; identifying at least one statisticregarding at least one type of outcome; determining at least one set ofprobability and odds associated with the type of outcome; receiving atleast one information request from the wagering application and send atleast one information request to the wagering application; and receivingat least one wager selection and at least one wager confirmation fromthe wagering application.